策略性补偿下混合渠道下游企业创新激励研究

电子商务环境下,以创新降低物流和库存等分销成本是零售商赢得生存与发展的重要途径,在渠道和谐和利润最大化的双重驱使下,策略性创新补偿成为制造商的首选策略.针对传统零售渠道与在线直销渠道的价格竞争,利用Stackelberg博弈,研究创新投入及其策略性补偿对混合渠道供应链的影响.结果表明,当零售商以创新投入降低其物流和库存等边际分销成本时,制造商将维持批发价和在线直销价不变,零售商将降低传统零售价,制造商和零售商的利润都得到改进;当制造商对零售商实施一定补偿以激励零售商进行更大的创新投入时,制造商将提高批发价并维持在线直销价不变,零售商将降低其零售价并增加创新投入,制造商和零售商的利润将得到进一步改善.因此,创新投入及其策略性补偿能实现混合渠道成员收益的Pareto改进.     

当代新闻叙事的“受众转向”及其“诗学功能”的生成

伴随着新媒体的崛起，普通人参与媒介叙述的潜能得到释放，受众在新闻叙事中的主体性得到彰显。从公共新闻到公民新闻的发展，使受众的新闻叙述控制权得到扩张。新媒体环境下普通受众参与的互动叙述从不同角度延伸新闻故事的情节线，使之成为一个开放性文本。“受众叙述”所具备的动态生成的特性，导致文本边界趋于消解，由此建构了新闻叙事的“全文本”。受众以“游猎”姿态展开新闻叙述，对主流媒体的新闻文本进行挪用、拼贴和戏仿，生成具有“杂交”性质的叙述文本，成为当下年轻世代新闻消费的“间接化”与“娱乐化”的重要表征。作为一种“生产者式文本”，受众叙述不再受制于新闻类别的“常规”，为新闻“诗学功能”的实现提供了更多可能，并在集体意义建构中发挥重要作用。     

Bayesian neural tree models for nonparametric regression

Frequentist and Bayesian methods differ in many aspects but share some basic optimal properties. In real-life prediction problems, situations exist in which a model based on one of the above paradigms is preferable depending on some subjective criteria. Nonparametric classification and regression techniques, such as decision trees and neural networks, have both frequentist (classification and regression trees (CARTs) and artificial neural networks) as well as Bayesian counterparts (Bayesian CART and Bayesian neural networks) to learning from data. In this paper, we present two hybrid models combining the Bayesian and frequentist versions of CART and neural networks, which we call the Bayesian neural tree (BNT) models. BNT models can simultaneously perform feature selection and prediction, are highly flexible, and generalise well in settings with limited training observations. We study the statistical consistency of the proposed approaches and derive the optimal value of a vital model parameter. The excellent performance of the newly proposed BNT models is shown using simulation studies. We also provide some illustrative examples using a wide variety of standard regression datasets from a public available machine learning repository to show the superiority of the proposed models in comparison to popularly used Bayesian CART and Bayesian neural network models.     

不充分就业感知对员工兼职创业投入的影响机制研究

基于情感-认知评价的理论视角,在控制了工作满意度路径之后,通过对311名兼职创业者的实证数据进行分析,探究了员工不充分就业感知的两个维度——资质过剩感和成长缺乏感对员工兼职创业投入的作用机理。研究结果表明:资质过剩感、成长缺乏感以及两者的交互作用会强化员工与本职工作的疏离感;工作疏离感正向影响员工兼职创业投入;工作疏离感在资质过剩感、成长缺乏感及其交互作用对兼职创业投入的影响中发挥了中介作用。